R Reference Card for Data Mining
Yanchang Zhao,https://www.sodocs.net/doc/bf1752261.html,,August 15,2013
-See the latest version
at https://www.sodocs.net/doc/bf1752261.html, .Click the link also for document
R and Data Mining:Examples and Case Studies .-The package names are in parentheses.
-Recommended packages and functions are shown in bold.-Click a package in this PDF
?le to ?nd it on CRAN.
apriori()mine associations with APRIORI algorithm –a level-wise,
breadth-?rst algorithm which counts transactions to ?nd frequent item-sets (arules )
eclat()mine frequent itemsets with the Eclat algorithm,which employs
equivalence classes,depth-?rst search and set intersection instead of counting (arules )
cspade()mine frequent sequential patterns with the cSPADE algorithm (aru-lesSequences )
seqefsub()search for frequent subsequences (TraMineR )
Packages
arules mine frequent itemsets,maximal frequent itemsets,closed frequent item-Apriori and Eclat.mine frequent sequences of states or events
ctree()conditional inference trees,recursive partitioning for continuous,cen-sored,ordered,nominal and multivariate response variables in a condi-tional inference framework (party )
rpart()recursive partitioning and regression trees (rpart )
mob()model-based recursive partitioning,yielding a tree with ?tted models as-sociated with each terminal node (party )
Random Forest
cforest()random forest and bagging ensemble (party )randomForest()random forest (randomForest )importance()variable importance (randomForest )varimp()variable importance (party )
Neural Networks
nnet()?t single-hidden-layer neural network (nnet )
mlp(),dlvq(),rbf(),rbfDDA(),elman(),jordan(),som(),
art1(),art2(),artmap(),assoz()various types of neural networks (RSNNS )
neuralnet training of neural networks (neuralnet )
Support Vector Machine (SVM)
svm()train a support vector machine for regression,classi?cation or density-estimation (e1071)
ksvm()support vector machines (kernlab )
Performance Evaluation
performance()provide various measures for evaluating performance of pre-diction and classi?cation models (ROCR )
PRcurve()precision-recall curves (DMwR )CRchart()cumulative recall charts (DMwR )roc()build a ROC curve (pROC )
auc()compute the area under the ROC curve (pROC )ROC()draw a ROC curve (DiagnosisMed )
Packages
party recursive partitioning
rpart recursive partitioning and regression trees
randomForest classi?cation and regression based on a forest of trees using ran-dom inputs
ROCR visualize the performance of scoring classi?ers
rpartOrdinal ordinal classi?cation trees,deriving a classi?cation tree when the response to be predicted is ordinal rpart.plot plots rpart models
pROC display and analyze ROC curves
nnet feed-forward neural networks and multinomial log-linear models
RSNNS neural networks in R using the Stuttgart Neural Network Simulator (SNNS)
neuralnet training of neural networks using backpropagation,resilient backprop-weight backtracking
lm()linear regression
glm()generalized linear regression predict()predict with models
residuals()residuals,the difference between observed values and ?tted val-ues
nls()non-linear regression
gls()?t a linear model using generalized least squares (nlme )
gnls()?t a nonlinear model using generalized least squares (nlme )
Packages
mixed effects models
Clustering
partition the data into k groups ?rst and then try to improve the quality of clus-tering by moving objects from one group to another
kmeans()perform k-means clustering on a data matrix
kmeansruns()call kmeans for the k-means clustering method and includes
estimation of the number of clusters and ?nding an optimal solution from several starting points (fpc )
pam()the Partitioning Around Medoids (PAM)clustering method (cluster )pamk()the Partitioning Around Medoids (PAM)clustering method with esti-mation of number of clusters (fpc )
kmeansCBI()interface function for kmeans (fpc )
cluster.optimal()search for the optimal k-clustering of the dataset
(bayesclust )
clara()Clustering Large Applications (cluster )
fanny(x,k,...)compute a fuzzy clustering of the data into k clusters (cluster )kcca()k-centroids clustering (?exclust )
ccfkms()clustering with Conjugate Convex Functions (cba )
apcluster()af?nity propagation clustering for a given similarity matrix (ap-cluster )
apclusterK()af?nity propagation clustering to get K clusters (apcluster )
cclust()Convex Clustering,incl.k-means and two other clustering algorithms
(cclust )
KMeansSparseCluster()sparse k-means clustering (sparcl )
tclust(x,k,alpha,...)trimmed k-means with which a proportion alpha of
observations may be trimmed (tclust )
Hierarchical Clustering
a hierarchical decomposition of data in either bottom-up (agglomerative)or top-down (divisive)way
hclust()hierarchical cluster analysis on a set of dissimilarities
birch()the BIRCH algorithm that clusters very large data with a CF-tree (birch )pvclust()hierarchical clustering with p-values via multi-scale bootstrap re-sampling (pvclust )
agnes()agglomerative hierarchical clustering (cluster )diana()divisive hierarchical clustering (cluster )
mona()divisive hierarchical clustering of a dataset with binary variables only
(cluster )
rockCluster()cluster a data matrix using the Rock algorithm (cba )
proximus()cluster the rows of a logical matrix using the Proximus algorithm
(cba )
isopam()Isopam clustering algorithm (isopam )
flashClust()optimal hierarchical clustering (?ashClust )fastcluster()fast hierarchical clustering (fastcluster )
cutreeDynamic(),cutreeHybrid()detection of clusters in hierarchical clus-tering dendrograms (dynamicTreeCut )
HierarchicalSparseCluster()hierarchical sparse clustering (sparcl )
Model based Clustering
Mclust()model-based clustering (mclust )
HDDC()a model-based method for high dimensional data clustering (HDclassif )fixmahal()Mahalanobis Fixed Point Clustering (fpc )fixreg()Regression Fixed Point Clustering (fpc )
mergenormals()clustering by merging Gaussian mixture components (fpc )
Density based Clustering
generate clusters by connecting dense regions
dbscan(data,eps,MinPts,...)generate a density based clustering of
arbitrary shapes,with neighborhood radius set as eps and density thresh-old as MinPts (fpc )
pdfCluster()clustering via kernel density estimation (pdfCluster )
Other Clustering Techniques
mixer()random graph clustering (mixer )
nncluster()fast clustering with restarted minimum spanning tree (nnclust )orclus()ORCLUS subspace clustering (orclus )
Plotting Clustering Solutions
plotcluster()visualisation of a clustering or grouping in data (fpc )
bannerplot()a horizontal barplot visualizing a hierarchical clustering (cluster )
Cluster Validation
silhouette()compute or extract silhouette information (cluster )
cluster.stats()compute several cluster validity statistics from a clustering
and a dissimilarity matrix (fpc )
clValid()calculate validation measures for a given set of clustering algorithms
and number of clusters (clValid )
clustIndex()calculate the values of several clustering indexes,which can be
independently used to determine the number of clusters existing in a data set (cclust )
NbClust()provide 30indices for cluster validation and determining
the number
of clusters (NbClust )
Packages
cluster cluster analysis
fpc various methods for clustering and cluster validation mclust model-based
clustering and normal mixture modeling birch clustering very large datasets using the BIRCH algorithm pvclust hierarchical clustering with p-values apcluster Af?nity Propagation Clustering
cclust Convex Clustering methods,including k-means algorithm,On-line Up-date algorithm
and Neural Gas algorithm and calculation of indexes for ?nding the number of clusters in a data set
cba Clustering for Business Analytics,including clustering techniques such as Proximus and Rock
bclust Bayesian clustering using spike-and-slab hierarchical model,suitable for clustering high-dimensional data
biclust algorithms to ?nd bi-clusters in two-dimensional data clue cluster ensembles
clues clustering method based on local shrinking clValid validation of clustering results
clv cluster validation techniques,contains popular internal and external cluster validation methods for outputs produced by package cluster bayesclust tests/searches for signi?cant clusters in genetic data
clustsig signi?cant cluster analysis,tests to see which (if any)clusters are statis-procedure for a data set generation clustering via mutual clusters Clustering
multiple genomic data types (EMC)methods for clustering (EMM)for data stream clustering
boxplot.stats()$out list data points lying beyond the extremes of the
whiskers
lofactor()calculate local outlier factors using the LOF algorithm (DMwR
or dprep )
lof()a parallel implementation of the LOF algorithm (Rlof )
Packages
LOF algorithm
in one-dimensional data based on robust methods identifying outliers
ts()create time-series objects plot.ts()plot time-series objects
smoothts()time series smoothing (ast )
sfilter()remove seasonal ?uctuation using moving average (ast )
Decomposition
decomp()time series decomposition by square-root ?lter (timsac )decompose()classical seasonal decomposition by moving averages stl()seasonal decomposition of time series by loess tsr()time series decomposition (ast )
ardec()time series autoregressive decomposition (ArDec )
Forecasting
arima()?t an ARIMA model to a univariate time series predict.Arima()forecast from models ?tted by arima
auto.arima()?t best ARIMA model to univariate time series (forecast )forecast.stl(),forecast.ets(),forecast.Arima()
forecast time series using stl ,ets and arima models (forecast )
Packages
analysing univariate time series forecasts utilities (DTW)
and control program decomposition
linear,time-invariant,time series models
Corpus()build a corpus,which is a collection of text documents (tm )
tm map()transform text documents,e.g.,stemming,stopword removal (tm )tm filter()?ltering out documents (tm )
TermDocumentMatrix(),DocumentTermMatrix()construct a
term-document matrix or a document-term matrix (tm )
Dictionary()construct a dictionary from a character vector or a term-document matrix (tm )
findAssocs()?nd associations in a term-document matrix (tm )
findFreqTerms()?nd frequent terms in a term-document matrix (tm )stemDocument()stem words in a text document (tm )stemCompletion()complete stemmed words (tm )
termFreq()generate a term frequency vector from a text document (tm )stopwords(language)return stopwords in different languages (tm )
removeNumbers(),removePunctuation(),removeWords()re-move numbers,punctuation marks,or a set of words from a text docu-ment (tm )
removeSparseTerms()remove sparse terms from a term-document matrix (tm )textcat()n-gram based text categorization (textcat )
SnowballStemmer()Snowball word stemmers (Snowball )
LDA()?t a LDA (latent Dirichlet allocation)model (topicmodels )CTM()?t a CTM (correlated topics model)model (topicmodels )terms()extract the most likely terms for each topic (topicmodels )
topics()extract the most likely topics for each document (topicmodels )wordcloud()plot a word cloud (wordcloud )
comparison.cloud()plot a cloud comparing the frequencies of words
across documents (wordcloud )
commonality.cloud()plot a cloud of words shared across documents
(wordcloud )
Packages
graph(),graph.edgelist(),graph.adjacency(),
graph.incidence()create graph objects respectively from edges,an edge list,an adjacency matrix and an incidence matrix (igraph )
plot(),tkplot(),rglplot()static,interactive and 3D plotting of
graphs (igraph )
gplot(),gplot3d()plot graphs (sna )
vcount(),ecount()number of vertices/edges (igraph )V(),E()vertex/edge sequence of igraph (igraph )is.directed()whether the graph is directed (igraph )
are.connected()check whether two nodes are connected (igraph )
degree(),betweenness(),closeness(),transitivity()various cen-trality scores (igraph ,sna )
add.edges(),add.vertices(),delete.edges(),delete.vertices()
add and delete edges and vertices (igraph )
neighborhood()neighborhood of graph vertices (igraph ,sna )get.adjlist()adjacency lists for edges or vertices (igraph )
nei(),adj(),from(),to()vertex/edge sequence indexing (igraph )cliques(),largest.cliques(),maximal.cliques(),clique.number()
?nd cliques,https://www.sodocs.net/doc/bf1752261.html,plete subgraphs (igraph )
clusters(),no.clusters()maximal connected components of a graph and
the number of them (igraph )
https://www.sodocs.net/doc/bf1752261.html,munity(),https://www.sodocs.net/doc/bf1752261.html,munity()community detection
(igraph )
cohesive.blocks()calculate cohesive blocks (igraph )induced.subgraph()create a subgraph of a graph (igraph )%->%,%<-%,%--%edge sequence indexing (igraph )
get.edgelist()return an edge list in a two-column matrix (igraph )
read.graph(),write.graph()read and writ graphs from and to ?les
of various formats (igraph )
Packages
igraph network analysis and visualization sna social network analysis
statnet a set of tools for the representation,visualization,analysis and simulation of network data
egonet ego-centric measures in social network analysis snort social network-analysis on relational tables network tools to create and modify network objects
bipartite visualising bipartite networks and calculating some (ecological)indices blockmodeling generalized and classical blockmodeling of valued networks diagram visualising simple graphs (networks),plotting ?ow diagrams
NetCluster
clustering for networks
NetData network data for McFarland’s SNA R labs
NetIndices estimating network indices,including
trophic structure of foodwebs
in R
NetworkAnalysis statistical inference on populations of weighted or unweighted and
longitudinal
networks
geocode()geocodes a location using Google Maps (ggmap )
plotGoogleMaps()create a plot of spatial data on Google Maps (plot-GoogleMaps )
qmap()quick map plot (ggmap )
get map()queries the Google Maps,OpenStreetMap,or Stamen Maps server
for a map at a certain location (ggmap )
gvisGeoChart(),gvisGeoMap(),gvisIntensityMap(),
gvisMap()Google geo charts and maps (googleVis )
GetMap()download a static map from the Google server (RgoogleMaps )ColorMap()plot levels of a variable in a colour-coded map (RgoogleMaps )PlotOnStaticMap()overlay plot on background image of map tile
(RgoogleMaps )
TextOnStaticMap()plot text on map (RgoogleMaps )
spatial data as HTML map mushup over Google Maps on Google map tiles in R
with Google Maps and OpenStreetMap
of spatial and spatio-temporal objects in Google Earth based Clustering Summaries for spatial point patterns weighting schemes,statistics and models
summary()summarize data
describe()concise statistical description of data (Hmisc )boxplot.stats()box plot statistics
Analysis of Variance
aov()?t an analysis of variance model
anova()compute analysis of variance (or deviance)tables for one or more ?tted
model objects
Statistical Test
t.test()student’s t-test
prop.test()test of equal or given proportions binom.test()exact binomial test
Mixed Effects Models
lme()?t a linear mixed-effects model (nlme )
nlme()?t a nonlinear mixed-effects model (nlme )
Principal Components and Factor Analysis
princomp()principal components analysis prcomp()principal components analysis
Other Functions
var(),cov(),cor()variance,covariance,and correlation density()compute kernel density estimates
Packages
mixed effects models
plot()generic function for plotting
barplot(),pie(),hist()bar chart,pie chart and histogram boxplot()box-and-whisker plot
stripchart()one dimensional scatter plot dotchart()Cleveland dot plot
qqnorm(),qqplot(),qqline()QQ (quantile-quantile)plot coplot()conditioning plot
splom()conditional scatter plot matrices (lattice )pairs()a matrix of scatterplots
cpairs()enhanced scatterplot matrix (gclus )parcoord()parallel coordinate plot (MASS )
cparcoord()enhanced parallel coordinate plot (gclus )parallelplot()parallel coordinates plot (lattice )densityplot()kernel density plot (lattice )
contour(),filled.contour()contour plot
levelplot(),contourplot()level plots and contour plots (lattice )
smoothScatter()scatterplots with smoothed densities color representation;
capable of visualizing large datasets
sunflowerplot()a sun?ower scatter plot assocplot()association plot mosaicplot()mosaic plot
matplot()plot the columns of one matrix against the columns of another fourfoldplot()a fourfold display of a 2×2×k contingency table persp()perspective plots of surfaces over the x?y plane
cloud(),wireframe()3d scatter plots and surfaces (lattice )interaction.plot()two-way interaction plot
iplot(),ihist(),ibar(),ipcp()interactive scatter plot,histogram,bar
plot,and parallel coordinates plot (iplots )
pdf(),postscript(),win.metafile(),jpeg(),bmp(),
png(),tiff()save graphs into ?les of various formats
gvisAnnotatedTimeLine(),gvisAreaChart(),
gvisBarChart(),gvisBubbleChart(),
gvisCandlestickChart(),gvisColumnChart(),
gvisComboChart(),gvisGauge(),gvisGeoChart(),gvisGeoMap(),gvisIntensityMap(),
gvisLineChart(),gvisMap(),gvisMerge(),gvisMotionChart(),gvisOrgChart(),gvisPieChart(),gvisScatterChart(),gvisSteppedAreaChart(),gvisTable(),
gvisTreeMap()various interactive charts produced with the Google Visualisation API (googleVis )
gvisMerge()merge two googleVis charts into one (googleVis )
Packages
ggplot2an implementation of the Grammar of Graphics
googleVis an interface between R and the Google Visualisation API to create interactive charts
rCharts interactive javascript visualizations from R
lattice a powerful high-level data visualization system,with an emphasis on mul-tivariate data
transform()transform a data frame
scale()scaling and centering of matrix-like objects t()matrix transpose aperm()array transpose sample()sampling
table(),tabulate(),xtabs()cross tabulation stack(),unstack()stacking vectors
split(),unsplit()divide data into groups and reassemble reshape()reshape a data frame between “wide”and “long”format merge()merge two data frames;similar to database join operations aggregate()compute summary statistics of data subsets by()apply a function to a data frame split by factors
melt(),cast()melt and then cast data into the reshaped or aggregated
form you want (reshape )
complete.cases()?nd complete cases,i.e.,cases without missing values na.fail,na.omit,na.exclude,na.pass handle missing values
Packages
reshape ?exibly restructure and aggregate data
data.table extension of data.frame for fast indexing,ordered joins,assignment,data manipulation
save(),load()save and load R data objects
read.csv(),write.csv()import from and export to .CSV ?les
read.table(),write.table(),scan(),write()read and
write data
read.fwf()read ?xed width format ?les
write.matrix()write a matrix or data frame (MASS )
readLines(),writeLines()read/write text lines from/to a connection,
such as a text ?le
sqlQuery()submit an SQL query to an ODBC database (RODBC )sqlFetch()read a table from an ODBC database (RODBC )
sqlSave(),sqlUpdate()write or update a table in an ODBC database
(RODBC )
sqlColumns()enquire about the column structure of tables (RODBC )sqlTables()list tables on an ODBC connection (RODBC )
odbcConnect(),odbcClose(),odbcCloseAll()open/close con-nections to ODBC databases (RODBC )
dbSendQuery execute an SQL statement on a given database connection (DBI )dbConnect(),dbDisconnect()create/close a connection to a DBMS
(DBI )
Packages
RODBC ODBC database access
foreign read and write data in other formats,such as Minitab,S,SAS,SPSS,Stata,Systat,...
DBI a database interface (DBI)between R and relational DBMS RMySQL interface to the MySQL database
the JDBC interface
driver
database ?les from data frames
download.file()download a ?le from the Internet
xmlParse(),htmlParse()parse an XML
or HTML ?le (XML
)userTimeline(),homeTimeline(),mentions(),
retweetsOfMe()retrieve various timelines within the Twitter uni-
verse
(twitteR )
getUser(),lookupUsers()get information of Twitter users (twitteR )getFollowers(),getFollowerIDs(),getFriends(),
getFriendIDs()get a list of followers/friends or their IDs of a Twitter user (twitteR )
twListToDF()convert twitteR lists to data frames (twitteR )
Packages
interface for R documents
mapreduce()de?ne and execute a MapReduce job (rmr2)keyval()create a key-value object (rmr2)
from.dfs(),to.dfs()read/write R objects from/to ?le system (rmr2)
Packages
with R via MapReduce on a Hadoop cluster Distributed File System (HDFS)NoSQL HBase database
Integrated Processing Environment via HIVE query
cloud using Amazon’s Elastic Map Reduce (EMR)engine for using R scripts in Hadoop streaming via the MapReduce paradigm client interface for R
as.ffdf()coerce a dataframe to an ffdf (ff )
read.table.ffdf(),read.csv.ffdf()read data from a ?at ?le to an ffdf
object (ff )
write.table.ffdf(),write.csv.ffdf()write an ffdf object to a ?at ?le
(ff )
ffdfappend()append a dataframe or an ffdf to an existing ffdf (ff )
big.matrix()create a standard big.matrix ,which is constrained to available
RAM (bigmemory )
read.big.matrix()create a big.matrix by reading from an ASCII ?le (big-memory )
write.big.matrix()write a big.matrix to a ?le (bigmemory )
filebacked.big.matrix()create a ?le-backed big.matrix ,which may ex-ceed available RAM by using hard drive space (bigmemory )
mwhich()expanded “which”-like functionality (bigmemory )
Packages
ff memory-ef?cient storage of large data on disk and fast access functions ffbase basic statistical functions for package ff
?lehash a simple key-value database for handling large data g.data create and maintain delayed-data packages
BufferedMatrix a matrix data storage object held in temporary ?les biglm regression for data too large to ?t in memory
bigmemory manage massive matrices with shared memory and memory-mapped ?les
biganalytics extend the bigmemory package with various analytics
bigtabulate table-,tapply-,and split-like functionality for matrix and sfInit(),sfStop()initialize and stop the cluster (snowfall )sfLapply(),sfSapply(),sfApply()parallel versions of
lapply(),sapply(),apply()(snowfall )
foreach(...)%dopar%looping in parallel (foreach )
registerDoSEQ(),registerDoSNOW(),registerDoMC()register respec-tively the sequential,SNOW and multicore parallel backend with the foreach package (foreach ,doSNOW ,doMC )
Packages
snowfall usability wrapper around snow for easier development of parallel R programs
snow simple parallel computing in R
multicore parallel processing of R code on machines with multiple cores or CPUs
snowFT extension of snow supporting fault tolerant and reproducible applica-(Message-Passing Interface)Virtual Machine)
execution facilities for R
the multicore package for the snow package for the Rmpi package
for the multicore package backend for foreach Loops hosts,clusters or grids processes
to Weka,and enables to use the following Weka
Association rules:
Apriori(),Tertius()
Regression and classi?cation:
LinearRegression(),Logistic(),SMO()
Lazy classi?ers:
IBk(),LBR()
Meta classi?ers:
AdaBoostM1(),Bagging(),LogitBoost(),MultiBoostAB(),Stacking(),
CostSensitiveClassifier()
Rule classi?ers:
JRip(),M5Rules(),OneR(),PART()
Regression and classi?cation trees:
J48(),LMT(),M5P(),DecisionStump()
Clustering:
Cobweb(),FarthestFirst(),SimpleKMeans(),XMeans(),DBScan()
Filters:
Normalize(),Discretize()
Word stemmers:
IteratedLovinsStemmer(),LovinsStemmer()
Tokenizers:
WordTokenizer()
.jcall()call a Java method (rJava ).jnew()create a new Java object (rJava )
.jinit()initialize the Java Virtual Machine (JVM)(rJava )
.jaddClassPath()adds directories or JAR ?les to the class path (rJava )
Sweave()mixing text and R/S code for automatic report generation
in R R sessions drawing analysis visualisation
https://www.sodocs.net/doc/bf1752261.html,/svn-history/r76/Rpad_homepage/R-refcard.pdf or
https://www.sodocs.net/doc/bf1752261.html,/doc/contrib/Short-refcard.pdf
R Reference Card,by Jonathan Baron
https://www.sodocs.net/doc/bf1752261.html,/doc/contrib/refcard.pdf
R Functions for Regression Analysis,by Vito Ricci
https://www.sodocs.net/doc/bf1752261.html,/doc/contrib/Ricci-refcard-regression. pdf
https://www.sodocs.net/doc/bf1752261.html,
RDataMining Group on LinkedIn(3000+members):
https://www.sodocs.net/doc/bf1752261.html,
RDataMining on Twitter(1200+followers):
https://www.sodocs.net/doc/bf1752261.html,/rdatamining
suggest any relevant R pack-ages/functions,please feel free to email me
-Have added some functions for retrieving Twitter data.
2August2013:
-Recomended packages and functions are shown in bold.They are packages and functions that I use often and would like to recommand.
1August2013:
-Click a package in this PDF?le to?nd it on CRAN.
-A few packages for MapReduce and Hadoop have been added.